Predictive modeling is a statistics-based technique to predict future outcomes. It is equipped by different models and analyses like Classification Model, Clustering Model, Forecast Modeland, Outliers Model, etc which is applied to many ranges of cases. Predictive modeling is somehow based on machine learning.
With this modeling, you see ads only about a product or service you are interested in. With this technology, you only get information about relevant products. Even a disease can be detected with help of Predictive modeling by analyzing your historical medical record. The main aim of predictive modeling is to summarize and visualize past behavior and generate a model with the help of past data and predict future outcomes.
Predictive modeling is also referred to as predictive analytics. This technology is used by companies for becoming better in terms of sales and growth they are getting almost 100% response from their marketing strategies, predicting the needs of consumers, and engaging with specific goods.
Types of Algorithm used in Predictive modeling
Predictive analytics uses two algorithms-
1.Deep learning–Deep learning is an artificial intelligence (AI) algorithm that simulates the functioning of the human brain in data analysis and generates correlations for use in making a decision. Deep learning is a branch of machine learning in artificial intelligence it had networks capable of unsupervised learning from data that is unstructured or unidentifiable.
2.Machine learning-Machine learning ( ML) is an analysis of computational algorithms that automatically evolve over observation. It’s used as a branch of artificial intelligence. Machine learning algorithms construct a mathematical model based on sample data, known as “training data,” to make predictions or decisions without being specifically trained to do so. Machine learning algorithms are used in a wide range of applications, such as email filtering and computer vision, where it is difficult or impractical to create traditional algorithms to perform the necessary tasks.
Applications of Analytics Models
- Health care
- Auto insurance
- Algorithmic trading
- Customer relationship management
- Churn prevention
- Sales forecasting
- Quality improvement
- Risk assessment
Popular Predictive modeling and their application.
Decision Trees Model
Decision trees construct a classification or regression system in the tree structure. It disintegrates the data set into smaller subsets as, at about the same time, the related decision tree is steadily created. The final result is obtained as a tree with decision nodes and leaf nodes. Some examples where this model is used are-MachineDiagnosis, In healthcare, the Energy sector, Remote sensors.
The clustering model takes data and sorts it into various groups on the basis of common attributes. The ability to separate data into various datasets based on unique characteristics is especially useful for such purposes, such as marketing. Marketers, for example, will segment a prospective consumer base based on similar characteristics. It operates by using two kinds of clustering – hard and soft clustering. Hard clustering designates each data point as joining or not to a data cluster. Although soft clustering assigns data possibility when you join a cluster.
Classification models are one of the most popular predictive computational models. These models function by categorizing details based on historical records. Classification models are used in various industries because they can quickly be re-engineered with new data and can offer a broad overview to address questions. Classification models can be found in various fields, such as banking and retail, which is why they are so popular relative to other models.
The forecast model is one of the most popular predictive computational models. It handles metric value estimation by predicting the values of new data based on gaining historical data and learning. It is also used to create numerical values in historical data where none is detected. One of the main benefits of predictive analysis is the ability to input several parameters. For this reason, they are one of the most commonly used quantitative computational models in use. They are used for diverse sectors and corporate purposes. For example, a call center can estimate how many services calls it will get on a day, or a shoe store can measure the inventory it needs for the coming sales cycle using forecast analysis. Forecast models are popular because they’re extremely scalable.
Time series model
The Time series model focuses on data in which time is the input parameter. A sequence of data points is collected in this model. The time series model operates by using various data points (taken from the data for the previous year) to create a predictive metric that can forecast patterns over a given period. Usage cases for this model shall include the number of regular calls received in the last three months, revenue for the last 20 years, or the number of patients that have been in the hospital for the last six weeks. It shall also take into account the seasons of the year or incidents that may have an effect on the statistic.